1. Technical Field
The present application relates generally to computer-implemented optimization and control of refinery activities.
2. Description of the Related Art
Oil refining is a complex and valuable process. Since the introduction of computers into operating refineries, a goal has been to integrate the decision making of management with automatic actuation by control systems. For many years, refiners have been developing computer programs to optimize the operation of oil refineries. This computer-based optimization activity can be divided into three types of solutions: planning and scheduling, multivariable constraint control (MVC), and real-time modelling and unit optimization of various refinery operating units. To date, however, each of these types of solutions has inherent limitations or deficiencies that have inhibited the development of a satisfactory integrated solution.
A first type of solution is the use of a refinery planning and scheduling model, which has been implemented by many refineries. Refineries must plan their operations weeks and months in advance in order satisfy the long lead times required for purchase of crude oil supply and contracts for product delivery. This is done in part by solving a linear or non-linear programming model which generally uses a multi-period, steady-state, regression based model of the entire refinery operation to calculate a refinery optimum operation plan weeks and months into the future. The refinery planning and scheduling model is usually run bi-weekly or monthly and its main purpose is to determine the best average operating conditions for the planning period, typically the next two to four weeks, for each of the refinery units in order to assure satisfaction of required product properties such as the octane number of the gasoline produced, and refinery inventory constraints on storage tanks used to hold crude oil feed, intermediate streams, and the final products that are ultimately shipped by pipeline or in batches on boats, trucks and railcars.
A weakness of this planning and scheduling model is that it is a crude representation of the operation of the entire refinery, which is a complex set of chemical processes. Another is that the planning and scheduling model and its input data require manual update because the input data is not directly measurable in the process. The model in this solution represents an average of the period being modelled; so, for example, it cannot distinguish between daytime and nighttime operation when changes in ambient temperature can have a dramatic impact on the optimum operating point of the refinery. The inability to match the planning model to the current behavior of the process and the modelling of only average behavior often results in a planning model that significantly deviates from the actual refinery behavior. The planning model therefore requires specific know-how from the users of the model to adjust the solution so that it is achievable by the refinery operators. While planning and scheduling software has been developed to make the manual manipulation of the planning solutions easier than in the past, the planning and scheduling model's limitations are a cause of poor integration with actual operational decisions in the refinery.
A second solution used in refinery optimization that has received wide attention and implementation is multivariable constraint control (MVC). Multivariable regression models, which include pricing constraints, are derived from process data that is generated by conducting experiments on individual process units, such as the fluid catalytic cracking unit (FCCU). The models are solved using linear, but sometimes non-linear, optimizers to arrive at control parameters that are applied by controllers to each of the individual process units. Recent advances in MVC model identification have made these regression models very reliable and easily derivable even under closed-loop operation of the individual process units. The objective function pricing values are often supplied by the previously mentioned refinery planning and scheduling optimization.
A weakness of these MVC controllers is the nature of the model. Because it is derived from regression of operating data, there is some reduction in fidelity of the model to actual operation; and the optimization of the MVC model will not reliably extrapolate a solution outside of its experience since the model is derived from operating data. Another problem is that the pricing values generated by the scheduling and planning optimization that are fed into the MVC model are not generally directly usable by the MVC's optimizer due to differences in the problem structure. This requires manual reconciliation of the differences in pricing between planning and scheduling and MVC in order to obtain the desired results.
In response to these recognized weaknesses, refiners have attempted to implement a third type of optimization, unit optimization, in their refineries in the form of first-principles models of the refinery operating units such as Crude and Vacuum Distillation, fluid catalytic cracking (FCC) and Hydrocracking in order to calculate an accurate optimum for each individual process unit. These optimization models consist of detailed models based on first principles of chemistry and physics of reaction kinetics, tray by tray distillation, valves, heat exchangers and all of the process equipment in the process unit. In addition, the stream flows are disaggregated into very detailed representation of the molecules and compounds that are constituents of crude oil and its product streams, even though few of these components can be directly measured in the process streams. The resulting models are very large and complex sometimes consisting of as many as 60,000 equations for a single process operating unit.
In this configuration these unit optimizations sit in a hierarchy below the refinery planning and scheduling model and above the process unit MVCs. The purpose of these optimizers is to overcome a lack of fidelity in the MVC models. While these types of unit optimizers have reportedly been successfully implemented with initial reports of high profit, only a small number of refining companies have attempted to implement these optimizers due to the complexity of the models and the intensive effort of highly skilled engineers required to implement them. Even then, due to the complexity and difficulty of maintaining the models and their optimization, refineries have abandoned this solution and returned to the use of planning optimization and the MVC solution for refinery optimization. A further reported problem is that the increased profits in using complex unit optimizers of this type over the above-mentioned solutions can only be measured when unit optimization was initiated; after a period of operation, it becomes difficult to determine how much additional profit was being realized by this solution without actually turning off unit optimization and returning to the basic combination of planning optimization and MVC controllers. This problem is due to shifts in the operational baseline resulting from changes in the mix of crude oils being processed, and changes in the products and product distribution goals of the refinery.
Another deficiency in these unit optimizations is that they fail to capitalize on the opportunity to optimize inter-unit interaction. Because planning and scheduling optimization, in part, attempts to optimize inter-unit interactions, some refiners have attempted to link unit optimizations with each other to try to achieve refinery-wide optimization with the aforementioned first-principles models. However, these implementations are much larger, involving hundreds of thousands of equations, and are more complex and more difficult to maintain than a single unit optimization.
There have been attempts to link MVCs together by a coordinating linear program that receives its pricing from the planning optimization and integrates the operation of the MVCs. The main failing of this approach is in the limitations of the linear model of highly non-linear reaction processes such as the FCC. In a variation on this approach users have attempted to operate closed form kinetic models of such non-linear reaction processes on-line and calculate numeric derivatives of these models, which are then passed to the coordinating linear program in order to allow it to approximate the non-linear process behavior. One serious deficiency in this approach is that the closed form kinetic models are difficult to automatically match to current reaction process conditions. Another is that the derivatives computed for these models are calculated by sequentially perturbing each input and solving the model, which is a tedious and computationally burdensome process. Further, in order to ensure convergence of the kinetic models, the user must set wide convergence criteria, which may result in unreliable and noisy derivatives that change significantly from solution interval to solution interval. In practice, these noisy derivatives are not implemented and this solution reverts to be a fixed linear program coordinating MVCs, resulting in the loss of the benefit of having a first principles model while introducing significant complexity in the implementation.